Title :
Study of the Case of Learning Bayesian Network from Complete Data
Author :
Yonghui, Cao ; Hui, Liu
Author_Institution :
Sch. of Econ. & Manage., Henan Inst. of Sci. & Technol., Xinxiang, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
The process of learning Bayesian networks takes different forms in terms of whether the structure of the network is known and whether the variables are all observable. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data. In this paper, we introduce two cases of learning Bayesian networks from complete data: known structure and observable variables, unknown structure and observable variables.
Keywords :
belief networks; data handling; complete data; incomplete data; learning Bayesian networks; Bayesian methods; Computer network management; Economic forecasting; Knowledge acquisition; Knowledge management; Maximum likelihood estimation; Parameter estimation; Statistics; Tail; Technology management; Bayesian networks; Network Structure; Observable Variables;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.251